



'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :						List[str]	=  logging.get_logger(__name__)






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    List[Any]      ,					UpperCamelCase_    :    str			)       ->	List[str]:
		snake_case__	     =UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase_      ,					config=UpperCamelCase_			)
		snake_case__	     =downstream_dict['projector.weight']
		snake_case__	     =downstream_dict['projector.bias']
		snake_case__	     =downstream_dict['model.post_net.linear.weight']
		snake_case__	     =downstream_dict['model.post_net.linear.bias']
		return model






def 				a  (     UpperCamelCase_    :    Any      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    Tuple			)       ->	Optional[Any]:
		snake_case__	     =UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase_      ,					config=UpperCamelCase_			)
		snake_case__	     =downstream_dict['model.linear.weight']
		snake_case__	     =downstream_dict['model.linear.bias']
		return model






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    Optional[Any]      ,					UpperCamelCase_    :    str			)       ->	Dict:
		snake_case__	     =UniSpeechSatForXVector.from_pretrained(UpperCamelCase_      ,					config=UpperCamelCase_			)
		snake_case__	     =downstream_dict['connector.weight']
		snake_case__	     =downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
				snake_case__	     =downstream_dict[
				    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
				]
				snake_case__	     =downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

		snake_case__	     =downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
		snake_case__	     =downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
		snake_case__	     =downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
		snake_case__	     =downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
		snake_case__	     =downstream_dict['objective.W']
		return model






@torch.no_grad()
def 				a  (     UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    str      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    Optional[int]			)       ->	List[Any]:
		snake_case__	     =torch.load(UpperCamelCase_      ,					map_location='cpu'			)

		snake_case__	     =checkpoint['Downstream']

		snake_case__	     =UniSpeechSatConfig.from_pretrained(UpperCamelCase_			)
		snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(
		    UpperCamelCase_      ,					return_attention_mask=UpperCamelCase_      ,					do_normalize=UpperCamelCase_			)

		snake_case__	     =hf_config.architectures[0]
		if arch.endswith('ForSequenceClassification'			):
				snake_case__	     =convert_classification(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
		elif arch.endswith('ForAudioFrameClassification'			):
				snake_case__	     =convert_diarization(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
		elif arch.endswith('ForXVector'			):
				snake_case__	     =convert_xvector(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
		else:
				raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""			)

		if hf_config.use_weighted_layer_sum:
				snake_case__	     =checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(UpperCamelCase_			)
		hf_model.save_pretrained(UpperCamelCase_			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  argparse.ArgumentParser()
			parser.add_argument(
			    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
			)
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
			parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
			SCREAMING_SNAKE_CASE__     :						List[Any]	=  parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









'''simple docstring'''

import os

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}






def 				a  (     UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =0

		snake_case__	     =0
		while index < len(UpperCamelCase_			) - 1:
				snake_case__	     =SYMBOLS[numerals[index]]
				snake_case__	     =SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value






def 				a  (     UpperCamelCase_    :    int			)       ->	str:
		snake_case__	     =''

		snake_case__	     =num // 1000
		numerals += m_count * "M"
		num %= 1000

		snake_case__	     =num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		snake_case__	     =num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals






def 				a  (     UpperCamelCase_    :    str = "/p089_roman.txt"			)       ->	int:
		snake_case__	     =0

		with open(os.path.dirname(UpperCamelCase_			) + roman_numerals_filename			) as filea:
				snake_case__	     =filea.readlines()

		for line in lines:
				snake_case__	     =line.strip()
				snake_case__	     =parse_roman_numerals(UpperCamelCase_			)
				snake_case__	     =generate_roman_numerals(UpperCamelCase_			)
				savings += len(UpperCamelCase_			) - len(UpperCamelCase_			)

		return savings


if __name__ == "__main__":
			print(f"""{solution() = }""")




